Manual Bottlenecks in Aviation Document Workflows
Our client is an American company operating globally, specializing in organizing and coordinating private flights worldwide. However, their aviation sourcing operations relied heavily on manual document preparation and fragmented operational processes, limiting efficiency and scalability.
Aircraft turnaround operations involve multiple tightly coordinated activities, including refueling, baggage handling, cleaning, catering, and passenger boarding. As airport traffic increased, operational complexity made it more difficult to maintain punctuality and optimize stand utilization.
- Complex and interdependent ASR requirements: ASR documents involved numerous variables and operational constraints, making manual processing slow and error-prone.
- Adapting LLMs to aviation-specific workflows: General-purpose models lacked the domain understanding required to reliably support complex trip-planning logic.
- Ensuring seamless and cost-efficient integration: The solution needed to integrate with existing TripGrade and AWS infrastructure without major system changes or excessive deployment costs
A scalable solution was required to automate document generation, improve operational consistency, and streamline aviation sourcing workflows.
Building an AI-Powered Document Generation Solution
Addressing these challenges required a solution capable of processing live operational data, continuously updating predictions, and delivering actionable insights to airport operators in real time.
Our team engaged as a strategic engineering partner to design and implement a scalable AI-driven platform focused on operational efficiency and practical deployment.
1. Understanding Aviation-Specific Operational Requirements
The project began with in-depth consultations to understand the complexities of private flight planning and Aviation Sourcing Request (ASR) generation workflows.
A detailed discovery phase helped define business requirements, operational constraints, and the logic needed to support highly specialized aviation sourcing processes.
2. Evaluating and Selecting the Right AI Models
Initial testing was conducted using Amazon Bedrock models, including LLaMA 3 and Mistral, to support cost-efficient deployment within the AWS ecosystem. Although these models delivered promising accuracy, they did not meet real-time operational performance requirements consistently.
To improve responsiveness and reliability, the solution transitioned to OpenAI models, including GPT-3.5 Turbo and GPT-4, which provided stronger production performance for complex ASR generation tasks.
3. Adapting LLMs with Business-Specific Knowledge
Rather than relying solely on general-purpose language models, Addepto connected the platform directly to internal SQL databases containing operational and business data.
Custom logic layers were implemented to guide the LLM in retrieving and using structured information effectively, ensuring generated ASR documents aligned with real operational requirements and business rules
4. Building a Scalable Microservices Architecture on AWS
The platform was designed using a cloud-native microservices architecture powered by FastAPI and deployed on AWS infrastructure. The architecture enabled seamless integration with the client’s existing TripGrade environment while maintaining flexibility and scalability.
A modular approach simplified maintenance, accelerated deployment, and supported future expansion of AI-powered operational workflows.
5. Developing Agent Logic and Prompt Engineering Frameworks
Precise agent logic was implemented to map specific prompts to operational actions and document generation workflows. Prompt engineering techniques and dual-response validation mechanisms were introduced to improve consistency and reduce the risk of inaccurate outputs.
A structured validation layer helped ensure reliability across highly variable aviation sourcing scenarios.
6. Accelerating Development with ContentCheck
Addepto leveraged its proprietary ContentCheck tool to automate prompt testing, evaluation, and response quality analysis throughout the development lifecycle.
Automated testing significantly reduced development time while improving output consistency and accelerating iteration across AI workflows.
Modernizing ASR Workflows for Better-Organized Trip Execution
The partnership transformed manual ASR workflows into a faster, more intelligent, and operationally efficient planning process. Rather than replacing aviation operators, the AI-powered platform was designed to support their work by accelerating information retrieval, reducing repetitive manual effort, and minimizing the risk of documentation errors.
By reducing administrative overhead, operational teams can focus more on higher-value planning and coordination tasks that require human expertise and decision-making.
Before
- Manual ASR preparation and fragmented workflows
- Slow processing with risk of errors
After
- Automated ASR generation with improved speed and accuracy
- Streamlined, scalable document workflows
Automated ASR generation also improves the quality and completeness of trip preparation by ensuring that essential operational details are captured consistently across workflows. Faster and more accurate document processing enables better-organized trip execution while improving operational scalability across aviation sourcing operations.
As part of KMS Technology, Addepto continues to deliver enterprise-grade AI consulting services that help organizations modernize operational processes through practical, business-focused automation.
Ready to transform documentation with AI-powered automation? Contact us today!